The Experts below are selected from a list of 57 Experts worldwide ranked by ideXlab platform
Chris Aldrich - One of the best experts on this subject based on the ideXlab platform.
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@CaltransDist7 I'd also think you'd be doing more good to edit the Waze app map with Road/Ramp closures as needed. Even more so as you'll
2017Co-Authors: Chris AldrichAbstract:@CaltransDist7 I'd also think you'd be doing more good to edit the Waze app map with Road/Ramp closures as needed. Even more so as you'll
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caltransdist7 i d also think you d be doing more good to edit the waze app map with Road Ramp closures as needed even more so as you ll
2017Co-Authors: Chris AldrichAbstract:@CaltransDist7 I'd also think you'd be doing more good to edit the Waze app map with Road/Ramp closures as needed. Even more so as you'll
Cao Wei - One of the best experts on this subject based on the ideXlab platform.
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Notice of Retraction The real time classification of vehicle by combination of GA, PCA and Improved SVM
2010Co-Authors: Zhang Changjun, Chen Yuzong, Cao WeiAbstract:There are important significance and social benefit of the application for real-time classification by using of the combination of GA, PCA and Improved SVM in a Road Ramp. The eight test points were put on the both sides of the Road Ramp, extracted feature vectors. The acoustic and seismic signals were used to research the classification in real-time. Because the dimension of feature vectors is too high, GA and PCA were used to reduce the dimension of feature vectors, and then SVM and improved SVM ware used to classify the feature vector. The classification accuracy was greatly improved. The highest classification accuracy of acoustic and seismic signals obtained by experiments was 92.0% and 76.1%. The dimension of feature vectors of acoustic and seismic signals was meantime reduced to 26 and 21 respectively, and the corresponding ratio is 95% and 99%, and the corresponding classification accuracy of independent set was 87.5% and 71.3%. Experiment result shows that: The classification accuracy by use of the combination of GA, PCA and improved SVM method is much higher than the single PCA, GA as well as combination of both.
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Real Time Vehicle Classification by GA,PCA and Improved SVM
Journal of Eastern Liaoning University, 2010Co-Authors: Cao WeiAbstract:It is practically and socially significant to real-time classificate vehicles using GA,PCA and improved SVM.Eight test points were set up on both sides of a Road Ramp to collect feature vectors of passing vehicles.Real-time classification test was conducted with acoustic and seismic signals.Over-high dimension of feature vectors was reduced using GA and PCA.Then,the feature vectors were classified with SVM and improved SVM;thereby the classification accuracy was greatly improved.The highest classification accuracy of acoustic and seismic signals obtained by experiments was 92.0% and 76.1%.The dimension of feature vectors of acoustic and seismic signals was reduced to 26 and 21,respectively,with corresponding ratio of 95% and 99%.The corresponding classification accuracy of independent set was 87.5% and 71.3%.Experiment result shows that the classification accuracy using GA,PCA and improved SVM is much higher than that of using PCA,GA alone or using two of them.
D Prabhakar - One of the best experts on this subject based on the ideXlab platform.
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EFFECTIVE PLACEMENT OF DETECTORS AT DIAMOND INTERCHANGES. INTERIM REPORT
1994Co-Authors: D Prabhakar, C J Messer, D L WoodsAbstract:This report summarizes results of a study of detector placement on the diamond interchange frontage Road (Ramp) using the Texas Intersection Simulation Model. The criterion used for the optimization is the minimization of delay. A secondary criterion is to reduce the dilemma zone to a minimum. The results indicate that the first detector located along the frontage Road (Ramp) should be placed about 30 m (10 ft) back from the stop bar. The remainder of the layout should provide a vehicle interval between 1.1 and 1.3 seconds and not trap a vehicle at the design speed in a dilemma situation. The recommended layout of detectors achieves these two goals.
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Effective placement of detectors at diamond interchanges
1994Co-Authors: D PrabhakarAbstract:This report summarizes results of a study of detector placement on the diamond interchange frontage Road (Ramp) using the Texas Intersection Simulation Model. The criterion used for the optimization is the minimization of delay. A secondary criterion is to reduce the dilemma zone to a minimum. The results indicate that the first detector located along the frontage Road (Ramp) should be placed about 30 meters (100 feet) back from the stop bar. The remainder of the layout should provide a vehicle interval between 1.1 and 1.3 seconds and not trap a vehicle at the design speed in a dilemma situation. The recommended layout of detectors achieves these two goals.
Liao Yuan-ling - One of the best experts on this subject based on the ideXlab platform.
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Layout Model of Urban Elevation-Road Ramp
Journal of Tongji University, 2004Co-Authors: Liao Yuan-lingAbstract:Based on the characteristics of layout problem of urban elevation-Road Ramp,including the amount of Ramp and space and types of Ramp,after investigating the method for determining impact area of elevation-Road and its traffic demand,a bi-level programming model of Ramp layout was presented by using the idea of traffic network design problem(NDP).In the model,optional Ramp was regarded as optional link of NDP.The model is a problem of 0-1 programing whose constrain field is non-convex.To solve the problem,a genetic algorithm was applied.At last,an example was given.
Zhang Changjun - One of the best experts on this subject based on the ideXlab platform.
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Notice of Retraction The real time classification of vehicle by combination of GA, PCA and Improved SVM
2010Co-Authors: Zhang Changjun, Chen Yuzong, Cao WeiAbstract:There are important significance and social benefit of the application for real-time classification by using of the combination of GA, PCA and Improved SVM in a Road Ramp. The eight test points were put on the both sides of the Road Ramp, extracted feature vectors. The acoustic and seismic signals were used to research the classification in real-time. Because the dimension of feature vectors is too high, GA and PCA were used to reduce the dimension of feature vectors, and then SVM and improved SVM ware used to classify the feature vector. The classification accuracy was greatly improved. The highest classification accuracy of acoustic and seismic signals obtained by experiments was 92.0% and 76.1%. The dimension of feature vectors of acoustic and seismic signals was meantime reduced to 26 and 21 respectively, and the corresponding ratio is 95% and 99%, and the corresponding classification accuracy of independent set was 87.5% and 71.3%. Experiment result shows that: The classification accuracy by use of the combination of GA, PCA and improved SVM method is much higher than the single PCA, GA as well as combination of both.
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The Research of Vehicle Classification Using SVM and KNN in a Ramp
2009 International Forum on Computer Science-Technology and Applications, 2009Co-Authors: Zhang Changjun, Chen YuzongAbstract:There is an important significance of the application for real-time classification by using of the acoustic and seismic signals generated by vehicles in the Road Ramp. The eight test points were put on the both sides of a Road Ramp, the some devices of acoustic and seismic sensors etc were put in each point. On the acquisition of acoustic and seismic signals, shorttime Fourier transform (STFT) was used for feature extraction. In the classification, Radial Basis Function (RBF) kernel was used to train SVM, KNN and SVM were used for the comparative study of real-time classification and achieved good results. We also proposed an improved SVM algorithm which has improved the classification accuracy of SVM to nearly 1 percent. This paper also discussed the classification of the different window size, and discussed the influence on the classification accuracy changes in the window size. And it finally comes to the conclusion by experiment: it is obvious that the classification accuracy is sensitive to the window size. In the classification accuracies, the performance of SVM is superior to that of KNN, and improved SVM is slightly superior to SVM.
